State‐of‐charge prediction of lithium ion battery through multivariate adaptive recursive spline and principal component analysis-Reference-Cited by-同舟云学术

State‐of‐charge prediction of lithium ion battery through multivariate adaptive recursive spline and principal component analysis

Published:2020-03-13 Issue:2 Volume:3 Page:
ISSN:2578-4862
Container-title:Energy Storage
language:en
Short-container-title:Energy Storage

Author:

Vyas Mayank¹,Pareek Kapil¹^ORCID,Spare Shitanshu¹,Garg Akhil²^ORCID,Gao Liang²

Affiliation:

1. Centre for Energy & Environment Malaviya National Institute of Technology Jaipur India

2. State Key Lab of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/est2.147

Reference53 articles.

1. Design and analysis of capacity models for Lithium-ion battery

2. Melamine–terephthalaldehyde–lithium complex: a porous organic network based single ion electrolyte for lithium ion batteries

3. A New Predictive Model for the State-of-Charge of a High-Power Lithium-Ion Cell Based on a PSO-Optimized Multivariate Adaptive Regression Spline Approach

4. Coupling Analysis and Performance Study of Commercial 18650 Lithium-Ion Batteries under Conditions of Temperature and Vibration

5. Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications

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